mentorAI now natively supports Meta’s open‑weight Llama 3 family, giving universities full control over cost, data, and customization. Below is a concise look at how the integration works and why it matters.

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Llama 3 Models in mentorAI

• Llama 3 8B‑Instruct – lightweight, fast, and ideal for large‑scale student Q&A or discussion boards.

• Llama 3 70B‑Instruct – flagship open model offering near–GPT‑4 quality reasoning and a 32 k token window; perfect for writing feedback, coding help, and long‑context tutoring.

• Llama 3 405B (preview) – enterprise‑grade model available through managed clouds; excels at complex research synthesis and advanced STEM explanations.

All variants support tool‑calling, citations, and multilingual dialogue, and can be quantized for efficient GPU or CPU inference.

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Deployment and Routing

mentorAI treats every Llama model as a pluggable backend:

• Self‑hosted – run the open weights on campus GPU clusters or a private Kubernetes/VPC. mentorAI spins up a serving container and automatically routes traffic.

• Cloud endpoints – point mentorAI at Llama on AWS Bedrock, Azure AI Studio, GCP Vertex AI, Hugging Face Inference Endpoints, or Together.ai. No code changes—just switch the API key/URL.

• Hybrid – mix and match: cheap workloads on‑prem with 8B; heavy research routed to 70B/405B in the cloud.

Administrators map each mentor or course to a model; mentorAI’s middleware handles load‑balancing, batching, retries, and fail‑over transparently.

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Prompt Orchestration & Controls

• Persona & system prompts define tone (e.g., Socratic coach, lab TA).

• Context injection adds syllabi, rubrics, or PDFs; mentorAI can feed entire chapters thanks to Llama 3’s long context.

• Safety layers use Meta’s LlamaGuard plus mentorAI’s own filters to block disallowed content before it reaches students.

• Tool & function calls let Llama trigger external calculators, graders, or database look‑ups; mentorAI executes the call and returns results in‑stream.

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Monitoring, Cost, and Privacy

mentorAI logs every token, latency, and error, so universities can:

• Set per‑model quotas and budget alerts.

• Compare on‑prem vs. cloud cost per 1 k tokens.

• Audit conversations (encrypted at rest) for quality and compliance.

Because Llama weights are open, no student data ever leaves the institution unless you choose a cloud endpoint—and even then, data stays in your tenant.

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Why Llama Matters for Higher Ed

• Transparency & trust – open weights mean faculty can inspect and even fine‑tune the model on university content.

• Budget control – run locally to avoid usage fees or scale in the cloud only when needed.

• Customization – tailor a private Llama checkpoint to campus writing style, policies, or domain jargon.

• Future‑proof – as Meta releases new checkpoints, mentorAI can adopt them with a simple config change.

In short, mentorAI + Llama gives universities a powerful, open, and economically sustainable AI foundation—backed by the freedom to host, tune, and govern the model on their own terms.

Learn more at https://ibl.ai